[Paper Review] FairAIED: Navigating Fairness, Bias, and Ethics in Educational AI Applications

Zhipeng Yin, Sribala Vidyadhari Chinta+3 more|arXiv (Cornell University)|Jul 26, 2024

Ethics and Social Impacts of AI36 citations

TL;DR

A comprehensive survey of fairness, bias, and ethics in AI for education, outlining bias types, mitigation strategies, fairness metrics, and regulatory considerations.

ABSTRACT

The integration of AI in education holds immense potential for personalizing learning experiences and transforming instructional practices. However, AI systems can inadvertently encode and amplify biases present in educational data, leading to unfair or discriminatory outcomes. As researchers have sought to understand and mitigate these biases, a growing body of work has emerged examining fairness in educational AI. These studies, though expanding rapidly, remain fragmented due to differing assumptions, methodologies, and application contexts. Moreover, existing surveys either focus on algorithmic fairness without an educational setting or emphasize educational methods while overlooking fairness. To this end, this survey provides a comprehensive systematic review of algorithmic fairness within educational AI, explicitly bridging the gap between technical fairness research and educational applications. We integrate multiple dimensions, including bias sources, fairness definitions, mitigation strategies, evaluation resources, and ethical considerations, into a harmonized, education-centered framework. In addition, we explicitly examine practical challenges such as censored or partially observed learning outcomes and the persistent difficulty in quantifying and managing the trade-off between fairness and predictive utility, enhancing the applicability of fairness frameworks to real-world educational AI systems. Finally, we outline an emerging pathway toward fair AI-driven education and by situating these technologies and practical insights within broader educational and ethical contexts, this review establishes a comprehensive foundation for advancing fairness, accountability, and inclusivity in the field of AI education.

Motivation & Objective

• Identify and categorize the main forms of bias in educational AI (data-related, algorithmic, user-interaction).
• Summarize existing bias mitigation techniques and fairness interventions in AI for education.
• Review fairness notions and metrics used to evaluate educational AI systems.
• Discuss ethical principles, transparency, and regulatory frameworks shaping fair AI in education.
• Highlight datasets and tools commonly used in educational AI research and their biases.

Proposed method

• Literature review across IEEE Xplore, ACM DL, PubMed, Scopus, and Google Scholar with systematic screening and data extraction.
• Classification of biases into data-related, algorithmic, and user-interaction categories with case-study examples.
• Presentation of fairness notions (individual and group fairness) and corresponding evaluation metrics.
• Discussion of ethical frameworks and regulatory considerations guiding AI in education.

Experimental results

Research questions

• RQ1 What are the predominant bias types observed in AI applications for education?
• RQ2 What methods and metrics exist to mitigate bias and assess fairness in educational AI?
• RQ3 How do ethical considerations and regulatory frameworks shape the development and deployment of educational AI?
• RQ4 What are the practical challenges and trade-offs between fairness and accuracy in educational AI systems?

Key findings

• The survey identifies data-related, algorithmic, and user-interaction biases as fundamental barriers to fairness in educational AI.
• It outlines pre-processing, in-processing, and post-processing bias mitigation techniques and fairness interventions.
• It discusses fairness notions (individual vs. group) and corresponding metrics for evaluating educational AI systems.
• It emphasizes the balance between fairness and accuracy and the need for diverse datasets and collaborative approaches.
• It highlights ethical considerations and regulatory frameworks as essential to shaping equitable AI use in education.